Reading and writing to Delta Lake using Dask engine.

dask-deltatable is available on PyPI:

pip install dask-deltatable

And conda-forge:

conda install -c conda-forge dask-deltatable

1. Read the parquet files from Delta Lake and parallelize with Dask
2. Write Dask dataframes to Delta Lake (limited support)
3. Supports multiple filesystems (s3, azurefs, gcsfs)
4. Subset of Delta Lake features:
   • Time Travel
   • Schema evolution
   • Parquet filters
     • row filter
     • partition filter

1. Writing to Delta Lake is still in development.
2. optimize API to run a bin-packing operation on a Delta Table.

import dask_deltatable as ddt
# read delta table
df = ddt.read_deltalake("delta_path")
# with specific version
df = ddt.read_deltalake("delta_path", version=3)
# with specific datetime
df = ddt.read_deltalake("delta_path", datetime="2018-12-19T16:39:57-08:00")

df is a Dask DataFrame that you can work with in the same way you normally would. See the Dask DataFrame documentation for available operations.

To be able to read from S3, azure, gcsfs, and other remote filesystems, you ensure the credentials are properly configured in environment variables or config files. For AWS, you may need ~/.aws/credential; for gcsfs, GOOGLE_APPLICATION_CREDENTIALS. Refer to your cloud provider documentation to configure these.

ddt.read_deltalake("s3://bucket_name/delta_path", version=3)

dask-deltatable can connect to AWS Glue catalog to read the delta table. The method will look for AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables, and if those are not available, fall back to ~/.aws/credentials.

Example:

ddt.read_deltalake(catalog="glue", database_name="science", table_name="physics")

dask-deltatable can connect to Unity catalog to read the delta table. The method will look for DATABRICKS_HOST and DATABRICKS_TOKEN environment variables or try to find them as kwargs with the same name but lowercase.

Example:

ddt.read_unity_catalog(
    catalog_name="projects",
    schema_name="science",
    table_name="physics"
)

To write a Dask dataframe to Delta Lake, use to_deltalake method.

import dask.dataframe as dd
import dask_deltatable as ddt
df = dd.read_csv("s3://bucket_name/data.csv")
# do some processing on the dataframe...
ddt.to_deltalake("s3://bucket_name/delta_path", df)

Writing to Delta Lake is still in development, so be aware that some features may not work.